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Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map. [ABSTRACT FROM AUTHOR]

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building's level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning. [ABSTRACT FROM AUTHOR]